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Modeling the relationship between cervical cancer mortality and trace elements based on genetic algorithm-partial
1Department of Chemistry and Chemical Engineering, Yibin University, Yibin, People's Republic of China. chaotan1112@163.com
This study explored the link between cervical cancer mortality and soil trace elements in China. The findings suggest that a combination of genetic algorithm-partial least squares (GA-PLS) and least square support vector machine (LSSVM) can predict cancer mortality.
Area of Science:
- Environmental Science
- Oncology
- Geochemistry
Background:
- Cervical cancer mortality rates vary geographically.
- Soil trace elements are potential environmental factors influencing cancer risk.
- Understanding these relationships can aid in public health strategies.
Purpose of the Study:
- To investigate the association between soil trace elements and cervical cancer mortality in China.
- To identify key trace elements linked to cancer mortality.
- To develop and compare predictive models for cancer mortality based on trace elements.
Main Methods:
- Analysis of 25 soil trace elements across 23 regions in China.
- Utilized genetic algorithm-partial least squares (GA-PLS) for element selection.
- Developed and compared partial least squares (PLS) and least square support vector machine (LSSVM) models.
Main Results:
- Identified Bromine (Br), Tantalum (Ta), Lead (Pb), Chromium (Cr), and Arsenic (As) as significant trace elements.
- The LSSVM model demonstrated superior performance compared to the PLS model.
- GA-PLS effectively selected key predictive elements.
Conclusions:
- The combination of GA-PLS and LSSVM shows promise for predicting cervical cancer mortality.
- Soil trace element analysis can be a valuable tool in cancer research and prevention.
- Further research is warranted to validate these findings in diverse populations.
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